A method has been conventionally known in which causal relationships (called a causal network) among a plurality of events (elements) are represented by a table or graph for use in predicting the behavior in a complex system or deducing the cause of a problem occurring in the system. For example, in a Bayesian network, each element is treated as a random variable, and a causal relationship between elements is represented by a conditional probability. PTL 1 discloses an example of using a causal-loop diagram (CLD) in risk assessment of a software development project. In the causal-loop diagram, the notation is such that elements having a causal relationship are connected with an arc, the arc being added with a plus sign in the case of a positive correlation, a minus sign in the case of a negative correlation, or double lines in the case where there is a time delay until the effect appears. PTL 2 discloses an example in which, in the case where an abnormality has occurred in a large-scale plant, a cause-and-effect table associating an assumed cause of abnormality with an indication pattern of each surveillance index is referenced to identify the cause. PTL 3 discloses an example of preparing a causal network defining the order and interval of alarms to be generated by an interlock system upon occurrence of an abnormality in a large-scale plant, so that, when an alarm is actually generated, an operator can predict an alarm that is possibly generated next and measures to be taken in response thereto. PTL 4 shows a multi-layer causal network in which causal graphs (graphs in which elements having a positive correlation are connected with a “plus” arc and elements having a negative correlation are connected with a “minus” arc) for respective elapsed times t1, t2, and so forth are arranged in the order of time to represent changes in causal relationships among elements over time since the occurrence of an abnormality.